import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A						=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Optional[Any]:
		return torch.atana(UpperCamelCase			,       UpperCamelCase							) / math.pi * 2


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.sin(t * math.pi / 2							) ** 2
		_lowerCamelCase				   =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(UpperCamelCase			,       UpperCamelCase							)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		pass





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()

				_lowerCamelCase				   =  DiffusionAttnUnetaD(snake_case__     ,  n_attn_layers=4		)
				_lowerCamelCase				   =  deepcopy(self.diffusion		)
				_lowerCamelCase				   =  torch.quasirandom.SobolEngine(1     ,  scramble=snake_case__		)



def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    List[str]:
		_lowerCamelCase				   =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""							)

		return F"""./{model_name}.ckpt"""


A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A						=			{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
A						=			{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A						=			{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

A						=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    int:
		if name.startswith('skip'							):
				return name.replace('skip'			,       RES_CONV_MAP['skip']							)

		# name has to be of format main.{digit}
		if not name.startswith('main.'							):
				raise ValueError(F"""ResConvBlock error with {name}"""							)

		return name.replace(name[:6]			,       RES_CONV_MAP[name[:6]]							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(UpperCamelCase							) and not isinstance(UpperCamelCase			,       UpperCamelCase							):
						return name.replace(UpperCamelCase			,       UpperCamelCase							)
				elif name.startswith(UpperCamelCase							):
						return [name.replace(UpperCamelCase			,       UpperCamelCase							) for v in value]
		raise ValueError(F"""Attn error with {name}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int=13							)   ->    Optional[int]:
		_lowerCamelCase				   =  input_string

		if string.split('.'							)[0] == "timestep_embed":
				return string.replace('timestep_embed'			,       'time_proj'							)

		_lowerCamelCase				   =  0
		if string.startswith('net.3.'							):
				depth += 1
				_lowerCamelCase				   =  string[6:]
		elif string.startswith('net.'							):
				_lowerCamelCase				   =  string[4:]

		while string.startswith('main.7.'							):
				depth += 1
				_lowerCamelCase				   =  string[7:]

		if string.startswith('main.'							):
				_lowerCamelCase				   =  string[5:]

		# mid block
		if string[:2].isdigit():
				_lowerCamelCase				   =  string[:2]
				_lowerCamelCase				   =  string[2:]
		else:
				_lowerCamelCase				   =  string[0]
				_lowerCamelCase				   =  string[1:]

		if depth == max_depth:
				_lowerCamelCase				   =  MID_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  'mid_block'
		elif depth > 0 and int(UpperCamelCase							) < 7:
				_lowerCamelCase				   =  DOWN_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(UpperCamelCase							) > 7:
				_lowerCamelCase				   =  UP_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				_lowerCamelCase				   =  DEPTH_0_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase							) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'							):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""							)

		_lowerCamelCase				   =  string_left[1:]

		if "resnets" in new_layer:
				_lowerCamelCase				   =  convert_resconv_naming(UpperCamelCase							)
		elif "attentions" in new_layer:
				_lowerCamelCase				   =  convert_attn_naming(UpperCamelCase							)
				_lowerCamelCase				   =  new_string_left

		if not isinstance(UpperCamelCase			,       UpperCamelCase							):
				_lowerCamelCase				   =  prefix + '.' + new_layer + '.' + string_left
		else:
				_lowerCamelCase				   =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    int:
		_lowerCamelCase				   =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'							):
						# up- and downsample layers, don't have trainable weights
						continue

				_lowerCamelCase				   =  rename(UpperCamelCase							)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  transform_conv_attns(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				else:
						_lowerCamelCase				   =  v

		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		if len(UpperCamelCase							) == 1:
				if len(v.shape							) == 3:
						# weight
						_lowerCamelCase				   =  v[:, :, 0]
				else:
						# bias
						_lowerCamelCase				   =  v
		else:
				# qkv matrices
				_lowerCamelCase				   =  v.shape[0]
				_lowerCamelCase				   =  trippled_shape // 3
				for i in range(3							):
						if len(v.shape							) == 3:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

		_lowerCamelCase				   =  args.model_path.split('/'							)[-1].split('.'							)[0]
		if not os.path.isfile(args.model_path							):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				_lowerCamelCase				   =  download(UpperCamelCase							)

		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_rate']
		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_size']

		_lowerCamelCase				   =  Object()
		_lowerCamelCase				   =  sample_size
		_lowerCamelCase				   =  sample_rate
		_lowerCamelCase				   =  0

		_lowerCamelCase				   =  UNetaDModel(sample_size=UpperCamelCase			,       sample_rate=UpperCamelCase							)
		_lowerCamelCase				   =  diffusers_model.state_dict()

		_lowerCamelCase				   =  DiffusionUncond(UpperCamelCase							)
		orig_model.load_state_dict(torch.load(args.model_path			,       map_location=UpperCamelCase							)['state_dict']							)
		_lowerCamelCase				   =  orig_model.diffusion_ema.eval()
		_lowerCamelCase				   =  orig_model.state_dict()
		_lowerCamelCase				   =  rename_orig_weights(UpperCamelCase							)

		_lowerCamelCase				   =  set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
		_lowerCamelCase				   =  set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

		assert len(UpperCamelCase							) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'							) for k in list(UpperCamelCase							)							), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						_lowerCamelCase				   =  value.squeeze()

				_lowerCamelCase				   =  value

		diffusers_model.load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  1_00
		_lowerCamelCase				   =  33

		_lowerCamelCase				   =  IPNDMScheduler(num_train_timesteps=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(UpperCamelCase							)
		_lowerCamelCase				   =  torch.randn([1, 2, config.sample_size]			,       generator=UpperCamelCase							).to(UpperCamelCase							)

		_lowerCamelCase				   =  torch.linspace(1			,       0			,       steps + 1			,       device=UpperCamelCase							)[:-1]
		_lowerCamelCase				   =  get_crash_schedule(UpperCamelCase							)

		_lowerCamelCase				   =  DanceDiffusionPipeline(unet=UpperCamelCase			,       scheduler=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(33							)
		_lowerCamelCase				   =  pipe(num_inference_steps=UpperCamelCase			,       generator=UpperCamelCase							).audios

		_lowerCamelCase				   =  sampling.iplms_sample(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       {}							)
		_lowerCamelCase				   =  generated.clamp(-1			,       1							)

		_lowerCamelCase				   =  (generated - audio).abs().sum()
		_lowerCamelCase				   =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path							)

		print('Diff sum'			,       UpperCamelCase							)
		print('Diff max'			,       UpperCamelCase							)

		assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				A						=			parser.parse_args()

				main(args)
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A						=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default='cifar10'     ,metadata={'help': 'Name of a dataset from the datasets package'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The column name of the images in the files.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the training data.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the validation data.'}			)
		lowerCAmelCase_     =   field(
		    default=0.1_5     ,metadata={'help': 'Percent to split off of train for validation.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  {}
				if self.train_dir is not None:
						_lowerCamelCase				   =  self.train_dir
				if self.validation_dir is not None:
						_lowerCamelCase				   =  self.validation_dir
				_lowerCamelCase				   =  data_files if data_files else None





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
		lowerCAmelCase_     =   field(
		    default='main'     ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Name or path of preprocessor config.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=0.7_5     ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=1e-3     ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)



def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  torch.stack([example['pixel_values'] for example in examples]							)
		return {"pixel_values": pixel_values}


def 	lowerCamelCase					(	)   ->    Dict:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_lowerCamelCase				   =  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
		if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'			,       UpperCamelCase			,       UpperCamelCase							)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout							)]			,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_lowerCamelCase				   =  training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase							)
		transformers.utils.logging.set_verbosity(UpperCamelCase							)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
		logger.info(F"""Training/evaluation parameters {training_args}"""							)

		# Detecting last checkpoint.
		_lowerCamelCase				   =  None
		if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
				_lowerCamelCase				   =  get_last_checkpoint(training_args.output_dir							)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'							)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
		_lowerCamelCase				   =  load_dataset(
		    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		_lowerCamelCase				   =  None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split			,       UpperCamelCase							) and data_args.train_val_split > 0.0:
				_lowerCamelCase				   =  ds['train'].train_test_split(data_args.train_val_split							)
				_lowerCamelCase				   =  split['train']
				_lowerCamelCase				   =  split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_lowerCamelCase				   =  {
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.config_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'							)
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""							)
						config.update_from_string(model_args.config_overrides							)
						logger.info(F"""New config: {config}"""							)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }							)

		# create image processor
		if model_args.image_processor_name:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path							)			,       config=UpperCamelCase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
		else:
				logger.info('Training new model from scratch'							)
				_lowerCamelCase				   =  ViTMAEForPreTraining(UpperCamelCase							)

		if training_args.do_train:
				_lowerCamelCase				   =  ds['train'].column_names
		else:
				_lowerCamelCase				   =  ds['validation'].column_names

		if data_args.image_column_name is not None:
				_lowerCamelCase				   =  data_args.image_column_name
		elif "image" in column_names:
				_lowerCamelCase				   =  'image'
		elif "img" in column_names:
				_lowerCamelCase				   =  'img'
		else:
				_lowerCamelCase				   =  column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_lowerCamelCase				   =  image_processor.size['shortest_edge']
		else:
				_lowerCamelCase				   =  (image_processor.size['height'], image_processor.size['width'])
		_lowerCamelCase				   =  Compose(
		    [
		        Lambda(lambda UpperCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
		        RandomResizedCrop(UpperCamelCase			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC							),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std							),
		    ]							)

		def preprocess_images(UpperCamelCase     :				Tuple							):
				_lowerCamelCase				   =  [transforms(UpperCamelCase							) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'							)
				if data_args.max_train_samples is not None:
						_lowerCamelCase				   =  ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
				# Set the training transforms
				ds["train"].set_transform(UpperCamelCase							)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'							)
				if data_args.max_eval_samples is not None:
						_lowerCamelCase				   =  (
						    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
						)
				# Set the validation transforms
				ds["validation"].set_transform(UpperCamelCase							)

		# Compute absolute learning rate
		_lowerCamelCase				   =  (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_lowerCamelCase				   =  training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		_lowerCamelCase				   =  Trainer(
		    model=UpperCamelCase			,       args=UpperCamelCase			,       train_dataset=ds['train'] if training_args.do_train else None			,       eval_dataset=ds['validation'] if training_args.do_eval else None			,       tokenizer=UpperCamelCase			,       data_collator=UpperCamelCase			,       )

		# Training
		if training_args.do_train:
				_lowerCamelCase				   =  None
				if training_args.resume_from_checkpoint is not None:
						_lowerCamelCase				   =  training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_lowerCamelCase				   =  last_checkpoint
				_lowerCamelCase				   =  trainer.train(resume_from_checkpoint=UpperCamelCase							)
				trainer.save_model()
				trainer.log_metrics('train'			,       train_result.metrics							)
				trainer.save_metrics('train'			,       train_result.metrics							)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_lowerCamelCase				   =  trainer.evaluate()
				trainer.log_metrics('eval'			,       UpperCamelCase							)
				trainer.save_metrics('eval'			,       UpperCamelCase							)

		# Write model card and (optionally) push to hub
		_lowerCamelCase				   =  {
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**UpperCamelCase							)
		else:
				trainer.create_model_card(**UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Dict:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()